1 Learning objectives

  1. You can use janitor::clean_names() to column names automatically.
  2. You can use rename() and rename_with() to clean column names manually.

2 Intro to the lesson

In R, column names are the “header” or “top” value of a column. They are used to refer to columns in the code, and serve as a default label in figures. They should have “clean”, standardized syntax so that we can work with them, and so that our code can be readable to other coders.

Ideally, column names:

  • should be short
  • should have no spaces or periods(space and periods should be replaced by underscore “_”)
  • should have no unusual characters(&, #, <, >)
  • should have a similar style

In this lesson, we will explore how to clean column names manually and automatically in R.

As a reminder, the data we are cleaning is the data from the COVID-19 serological survey conducted in Yaounde, Cameroon.

yaounde <- read_csv(here::here('ch02_data_cleaning_pipeline/data/yaounde_data.csv'))

3 Columns of our Data

We can use the names() function from base R. Or return to this chapter’s intro to have a look at the output of skim()

names(yaounde)
##  [1] "id ind"                    "AGE"                      
##  [3] "AGE.CATEGORY"              "SEX"                      
##  [5] "EDUCATION"                 "OCCUPATION"               
##  [7] "weight kg"                 "height cm"                
##  [9] "is.smoker"                 "is.pregnant"              
## [11] "is.medicated"              "household with_children"  
## [13] "breadwinner"               "source of_revenue"        
## [15] "has contact_COVID"         "igg.result"               
## [17] "igm result"                "symptoms.."               
## [19] "consultation"              "treatment..combinations"  
## [21] "drugsource"                "hospitalised"             
## [23] "sequelae"                  "respiration frequency."   
## [25] "is drug_parac"             "is drug_antibio"          
## [27] "is drug_hydrocortisone"    "is drug_other_anti_inflam"
## [29] "is drug_antiviral"         "is drug_chloro"           
## [31] "is drug_tradn"             "is drug_oxygen"           
## [33] "is drug_other"             "is drug_no_resp"          
## [35] "is drug_none"              "NA"

We can see that:

  1. Display the names of the typhoid dataset.

4 Automatically clean column names

4.1 janitor::clean_names()

A handy function for standardizing column names is the clean_names() from the {janitor} package.

The function clean_names(): -

  • Converts all names to consist of only underscores, numbers, and letters.

  • Parses letter cases and separators to a consistent format. (default is snake_case)

  • Handles special characters(&, #, <, >) or accented characters.

yaounde %>%
  clean_names() %>%
  names()
##  [1] "id_ind"                    "age"                      
##  [3] "age_category"              "sex"                      
##  [5] "education"                 "occupation"               
##  [7] "weight_kg"                 "height_cm"                
##  [9] "is_smoker"                 "is_pregnant"              
## [11] "is_medicated"              "household_with_children"  
## [13] "breadwinner"               "source_of_revenue"        
## [15] "has_contact_covid"         "igg_result"               
## [17] "igm_result"                "symptoms"                 
## [19] "consultation"              "treatment_combinations"   
## [21] "drugsource"                "hospitalised"             
## [23] "sequelae"                  "respiration_frequency"    
## [25] "is_drug_parac"             "is_drug_antibio"          
## [27] "is_drug_hydrocortisone"    "is_drug_other_anti_inflam"
## [29] "is_drug_antiviral"         "is_drug_chloro"           
## [31] "is_drug_tradn"             "is_drug_oxygen"           
## [33] "is_drug_other"             "is_drug_no_resp"          
## [35] "is_drug_none"              "na"

From this output, we can see that:

  • upper case variable names were converted to lower case (e.g., EDUCATION is now education)

  • spaces inside the variable names have been converted to underscores (e.g., id ind is now id_ind)

  • periods(.) have all been replaced by underscores (e.g., is.smoker is now is_smoker)

  1. Use the clean_names() function from {janitor} to clean the variables names typhoid dataset. Do not display the column names

5 Manually cleaning column names

We can also rename columns manually, either as an alternative to the automatic procedure described above or in addition to the automatic step above.

We will first illustrate how to manually clean column names as an alternative to the automatic procedure using the functions rename() and rename_with().

5.1 rename() and rename_with()

The rename() function is simply a way to change variable names. It was covered in our data wrangling chapter.

As a reminder, its syntax is so: rename(new_name = old_name)

Here, manually, we could rename weight kg to weight, height cm to height, is.smoker to smoker, is.pregnant to pregnant and is.medicated to medicated.

yaounde %>% rename(weight = `weight kg`,
                   height = `height cm`,
                   smoker = is.smoker,
                   pregnant = is.pregnant,
                   medicated = is.medicated) %>%
            names()

But this would make a long list if we have a dataset with 300 columns. Let’s see how to do so with rename_with().

Instead of changing each column individually, rename_with is a handy shortcut to do the same modification to all (or a collection of) column names.

The arguments of rename_with are: a data frame

  • a function to apply to each column name

  • a selection of column to rename (by default all columns)

Say, for example, we want all the column names to be in lower case, we can either change all the columns in upper case to lower case, 1 at a time (which would be a time consuming exercise), or we can use the rename_with function;

yaounde %>% 
  rename_with(tolower) %>%
  names()
##  [1] "id ind"                    "age"                      
##  [3] "age.category"              "sex"                      
##  [5] "education"                 "occupation"               
##  [7] "weight kg"                 "height cm"                
##  [9] "is.smoker"                 "is.pregnant"              
## [11] "is.medicated"              "household with_children"  
## [13] "breadwinner"               "source of_revenue"        
## [15] "has contact_covid"         "igg.result"               
## [17] "igm result"                "symptoms.."               
## [19] "consultation"              "treatment..combinations"  
## [21] "drugsource"                "hospitalised"             
## [23] "sequelae"                  "respiration frequency."   
## [25] "is drug_parac"             "is drug_antibio"          
## [27] "is drug_hydrocortisone"    "is drug_other_anti_inflam"
## [29] "is drug_antiviral"         "is drug_chloro"           
## [31] "is drug_tradn"             "is drug_oxygen"           
## [33] "is drug_other"             "is drug_no_resp"          
## [35] "is drug_none"              "na"

Here we see that all columns such as AGE and SEX etc. have been transformed to lower case.

  1. Manually rename the following column names in the typhoid dataset; CaseorControland Levelofeducation to case_control, education_level, respectively.

  2. Convert all the column names in the typhoid dataset to lower case

str_replace

The rename_with function can also be used for more complex operations, such as, modifying column names with unwanted characters (whitespaces, special characters).

We use a new function called str_replace to replace the whitespaces from our column names with an underscore.

The function allows us to replace matched patterns in a string. The arguments are:

  • a pattern such as "is." or "-"

  • a replacement pattern

Let’s build on what we did before. We corrected for upper cases, now let’s correct whitespaces.

yaounde %>% 
  rename_with(tolower) %>%
  rename_with(str_replace, pattern = " ",replacement = "_") %>%
  names()
##  [1] "id_ind"                    "age"                      
##  [3] "age.category"              "sex"                      
##  [5] "education"                 "occupation"               
##  [7] "weight_kg"                 "height_cm"                
##  [9] "is.smoker"                 "is.pregnant"              
## [11] "is.medicated"              "household_with_children"  
## [13] "breadwinner"               "source_of_revenue"        
## [15] "has_contact_covid"         "igg.result"               
## [17] "igm_result"                "symptoms.."               
## [19] "consultation"              "treatment..combinations"  
## [21] "drugsource"                "hospitalised"             
## [23] "sequelae"                  "respiration_frequency."   
## [25] "is_drug_parac"             "is_drug_antibio"          
## [27] "is_drug_hydrocortisone"    "is_drug_other_anti_inflam"
## [29] "is_drug_antiviral"         "is_drug_chloro"           
## [31] "is_drug_tradn"             "is_drug_oxygen"           
## [33] "is_drug_other"             "is_drug_no_resp"          
## [35] "is_drug_none"              "na"

To replace special characters like the period (.), we adjust the syntax slightly by adding \\ before the special character.

yaounde %>% 
  #correcting for upper cases
  rename_with(tolower) %>%
  #correcting for white spaces
  rename_with(str_replace, pattern = " ",replacement = "_") %>%
  #correcting for periods
  rename_with(str_replace, pattern = "\\.",replacement = "_") %>%
  names()
##  [1] "id_ind"                    "age"                      
##  [3] "age_category"              "sex"                      
##  [5] "education"                 "occupation"               
##  [7] "weight_kg"                 "height_cm"                
##  [9] "is_smoker"                 "is_pregnant"              
## [11] "is_medicated"              "household_with_children"  
## [13] "breadwinner"               "source_of_revenue"        
## [15] "has_contact_covid"         "igg_result"               
## [17] "igm_result"                "symptoms_."               
## [19] "consultation"              "treatment_.combinations"  
## [21] "drugsource"                "hospitalised"             
## [23] "sequelae"                  "respiration_frequency_"   
## [25] "is_drug_parac"             "is_drug_antibio"          
## [27] "is_drug_hydrocortisone"    "is_drug_other_anti_inflam"
## [29] "is_drug_antiviral"         "is_drug_chloro"           
## [31] "is_drug_tradn"             "is_drug_oxygen"           
## [33] "is_drug_other"             "is_drug_no_resp"          
## [35] "is_drug_none"              "na"

Note, however, how only the first occurrence of . was replaced. If we want to replace all occurrences of . we can use the function str_replace_all.

Here we see that treatment..combinations has become treatment._combinations: still not ideal as a column name.

yaounde %>% 
  #correcting for upper cases
  rename_with(tolower) %>%
  #correcting for ALL white spaces
  rename_with(str_replace_all, pattern = " ",replacement = "_") %>%
  #correcting for ALL periods
  rename_with(str_replace_all, pattern = "\\.",replacement = "_") %>%
  names()
##  [1] "id_ind"                    "age"                      
##  [3] "age_category"              "sex"                      
##  [5] "education"                 "occupation"               
##  [7] "weight_kg"                 "height_cm"                
##  [9] "is_smoker"                 "is_pregnant"              
## [11] "is_medicated"              "household_with_children"  
## [13] "breadwinner"               "source_of_revenue"        
## [15] "has_contact_covid"         "igg_result"               
## [17] "igm_result"                "symptoms__"               
## [19] "consultation"              "treatment__combinations"  
## [21] "drugsource"                "hospitalised"             
## [23] "sequelae"                  "respiration_frequency_"   
## [25] "is_drug_parac"             "is_drug_antibio"          
## [27] "is_drug_hydrocortisone"    "is_drug_other_anti_inflam"
## [29] "is_drug_antiviral"         "is_drug_chloro"           
## [31] "is_drug_tradn"             "is_drug_oxygen"           
## [33] "is_drug_other"             "is_drug_no_resp"          
## [35] "is_drug_none"              "na"

But even with this, we still have weird column names such as symptoms__ or treatment__combinations. These names are cleaned automatically by {janitor}’s clean_names() to symptoms and treatment_combinations: remember to use this function first! It will save you a lot of trouble/manipulations.

Automatic then manual cleaning of column names

Evidently, manually cleaning column names can be a cumbersome task, particularly when cleaning a large dataset with many variables. A combination of the automatic and manual procedure is more desirable as it not only saves on time, but can help make our column names more readable. We can start with the automatic clean-up, then check there are no weird column names remaining (which would need manual cleanup).

Here is an example of combining automatic and manual cleaning: a more appropriate cleanup would be to standardise the column names, first, and then remove the prefix is_ from all columns.

yaounde %>% 
  # standardize column name syntax
  clean_names() %>%
  # manually re-name columns
  rename_with(str_replace_all, pattern = "is_",replacement = "")

Now let’s save this first step of cleaning as a dataset.

It is good practice to save your dataset regularly in different steps of cleaning. In case you should find later on that your cleaning generated a data manipulation error and need to go back through your cleaning steps !

yaounde_cleaned_column_names <- 
  yaounde %>% 
  # standardize column name syntax
  clean_names() %>%
  # manually re-name columns
  rename_with(str_replace_all, pattern = "is_",replacement = "")

write_csv(yaounde_cleaned_column_names,
          here::here('ch02_data_cleaning_pipeline/data/yaounde_data_clean_names.csv'))
  1. Standardize the column names in the typhoid dataset then;

  • replace or_ with _

  • replace of with _

  • rename variables below10years n1119years n2035years n3644years, n4565years above65years to num_below_10_yrs num_11_19_yrs num_20_35_yrs num_36_44_yrs, num_45_65_yrs num_above_65_yrs

6 Wrapping up

In this lesson, we have been able to automatically and manually clean column names. This is only the first step to fixing the structural errors in our data. The next step would be removing any empty rows or columns from the data. We will be tackling this in the next lesson.

Contributors

The following team members contributed to this lesson:

References

Some material in this lesson was adapted from the following sources:

Artwork was adapted from: